AGE-SPECIFIC POPULATION ATTRIBUTABLE FRACTIONS FOR THE IMPACT OF HYPERTENSION ON INCIDENT DEMENTIA

Abstract The population attributable fraction (PAF) of dementia in United States attributable to hypertension is estimated at 7-9%. However, prior studies of the PAF might underestimate hypertension’s impact on dementia risk, given specific design limitations. Additionally, prior work neither accounted for changing prevalence/effects of hypertension with age nor considered early vs. later occurring dementia. Here, we quantified PAFs of dementia by age 80 from hypertension measured at midlife and late-life using individual-level data from the Atherosclerosis Risk in Communities Study. Incident dementia was determined by adjudicated review, telephone interviews, informant interviews, hospitalization records, and death certificates. We used measures of systolic and diastolic blood pressure (SBP, DBP) to define BP categories: normal BP (SBP < 120 and DBP < 80), elevated BP (SBP 120-129 and DBP < 80), stage 1 hypertension (SBP 130-139 or DBP 80-89), and stage 2 hypertension (SBP ≥140 or DBP ≥90); we also defined an overall non-normal BP value (SBP ≥120 or DBP ≥80). We generated hazard ratios (HRs) of 32-year incident dementia from Cox regression models that adjusted for demographic and clinical characteristics, and stratified by age at BP ascertainment (45-54, 55-64, 65-74). We then used the HRs to quantify PAFs of dementia from hypertension. We estimated that 15-20% of dementia cases by age 80 were attributable to non-normal BP. The strongest PAFs (12-21%) were from stage 2 hypertension. Current estimates of the dementia risk attributable to hypertension may be low. Targeting hypertension from midlife through early late-life could sizably reduce dementia risk.


MAINTENANCE OF HEALTH BEHAVIOR CHANGE: RECOMMENDATIONS FOR FUTURE RESEARCH
Jaime Hughes 1 , Aliza Randazzo 2 , and Rahma Ajja 3 , 1. Wake Forest School of Medicine, North Carolina,United States,2.Wake Forest University School of Medicine, North Carolina,United States,3.Wake Forest University School of Medicine -Winston-Salem, North Carolina, Winston Salem, North Carolina, United States Modifying poor health behaviors can enhance function, improve quality-of-life, and reduce morbidity and mortality among older adults.However, achieving and sustaining behavior change can be difficult.A growing body of research suggest that initiation and maintenance are unique constructs within health behavior change; each requiring unique skills and each associated with unique outcomes.Although some leading health behavior change models (e.g., Transtheoretical Model of Change) include maintenance as a construct, maintenance is often overlooked in the scientific literature.As part of a NIA Research Centers Collaborative Network (RCCN) pilot award, this project utilized a series of qualitative activities to explore the concept of maintenance and identify future recommendations for advancing the field.Three unique listening sessions were conducted with clinician-researchers (1 group) and community-based service organizations (2 groups).An interdisciplinary expert think tank was then convened to reflect on key findings.All sessions were conducted virtually; transcripts were reviewed using a priori themes with additional themes generated by an open coding approach.Major themes included the following: (1) To date, there is no consensus definition of maintenance nor is there agreement on maintenance across behaviors.(2) Few behavior change models consider how health behaviors must shift to accommodate for age-related changes in cognition and/or physical function.
(3) Existing funding mechanisms and timelines may not allow sufficient follow-up periods to examine maintenance over time.(4) Although the terms maintenance and sustainment are used interchangeably, community leaders emphasized the importance of sustaining health promotion programs in order to enable maintenance to occur.

EPIDEMIOLOGY AND COGNITIVE IMPAIRMENT/ DEMENTIA
Abstract citation ID: igad104.1293The population attributable fraction (PAF) of dementia in United States attributable to hypertension is estimated at 7-9%.However, prior studies of the PAF might underestimate hypertension's impact on dementia risk, given specific design limitations.Additionally, prior work neither accounted for changing prevalence/effects of hypertension with age nor considered early vs. later occurring dementia.Here, we quantified PAFs of dementia by age 80 from hypertension measured at midlife and late-life using individuallevel data from the Atherosclerosis Risk in Communities Study.Incident dementia was determined by adjudicated review, telephone interviews, informant interviews, hospitalization records, and death certificates.We used measures of systolic and diastolic blood pressure (SBP, DBP) to define BP categories: normal BP (SBP < 120 and DBP < 80), elevated BP (SBP 120-129 and DBP < 80), stage 1 hypertension (SBP 130-139 or DBP 80-89), and stage 2 hypertension (SBP ≥140 or DBP ≥90); we also defined an overall non-normal BP value (SBP ≥120 or DBP ≥80).We generated hazard ratios (HRs) of 32-year incident dementia from Cox regression models that adjusted for demographic and clinical characteristics, and stratified by age at BP ascertainment (45-54, 55-64, 65-74).We then used the HRs to quantify PAFs of dementia from hypertension.We estimated that 15-20% of dementia cases by age 80 were attributable to non-normal BP.The strongest PAFs (12-21%) were from stage 2 hypertension.Current estimates of the dementia risk attributable to hypertension may be low.Targeting hypertension from midlife through early late-life could sizably reduce dementia risk.

AGE-SPECIFIC POPULATION ATTRIBUTABLE FRACTIONS FOR THE IMPACT OF HYPERTENSION ON INCIDENT DEMENTIA
Abstract citation ID: igad104.1294

PREDICTING DEMENTIA DIAGNOSIS FROM COGNITIVE FOOTPRINTS IN ELECTRONIC MEDICAL RECORDS USING MACHINE LEARNING Jiayi Zhou, Hao Luo, Wenlong Liu, and Huiquan Zhou, The University of Hong Kong, Hong Kong, Hong Kong
The cognitive footprint theory suggests that a person's risk of dementia is affected by activities and events (footprints) across the lifespan.This study aimed to develop a clinical algorithm for predicting dementia diagnosis from cognitive footprints in electronic medical records (EMRs) using machine learning.Population-based EMRs from the Clinical Data Analysis and Reporting System in Hong Kong were employed.We included all patients with dementia diagnosed at 65+ between 2000 and 2018.Controls without dementia were matched (1:1) by age, sex, and index date.The prediction accuracy of a standard logistic regression model was compared with five machine learning models (LASSO, Random Forest, Multilayer perceptron, XGBoost, and LightGBM), using a wide range of established and exploratory risk factors identified at mid-( < 65 years) and late-life (65+ years).A total of 261,116 individuals (59.3% female; mean age at index date [SD]: 82.70 [8.54]) with and without dementia were included.Compared with the logistic model (area under the curve [AUC]: 0.60), the predictive and diagnostic accuracy of dementia improved substantially in machine learning models.The Multilayer perceptron and LightGBM models showed comparable performance with the same test AUC of 0.720.LASSO (AUC: 0.765) and Random Forest (AUC: 0.768) showed a slightly worse performance.Antipsychotic drugs, education, antidepressants and head injury in late life were the top 4 important predictors in all models.The machine learning-based algorithm for predicting dementia can be used to identify patients with an increased likelihood of dementia to allow precise and timely primary interventions.Evidence is accumulating that individuals with cancer diagnoses exhibit Alzheimer's disease (AD) and related dementia (ADRD) risk profiles that differ from the general population of U.S. older adults.In this study we used SEER-Medicare data to compare the relative risk of AD/ADRD between individuals with slow-progressive cancers and the non-cancer general population.The study cohort included individuals age 65+ (N=2,023,054) with a primary diagnosis (1999-2017) of one of nine slow progressive cancers (breast, colorectal, prostate, uterine, kidney, ovarian, and urinary bladder cancers, as well as lymphomas and melanoma) and no clinical record of AD/ADRD prior to cancer diagnosis.This cohort was then matched by age to a comparable non-cancer population (N=1,142,641).The hazard ratios of AD/ADRD for each cancer compared to the noncancer cohort were evaluated individually in 29 age-specific groups for each cancer type.All cancers had similar patterns of dependence for post-cancer AD/ADRD risks.We found that the presence of cancer was associated with higher risk of AD/ADRD at age at diagnosis 65-75; the relative risks decline with age at diagnoses becoming protective at advanced ages.Furthermore, for any given age at diagnosis the relative risk of AD/ADRD (i.e., cancer vs. non-cancer) also declines with time.Detailed discussion of possible causes of these effects including cancer treatment, genetic variation, possible trade-off effects, common risk and protective factors, possibly lower administration and adherence of AD/ADRD diagnostic procedures for individuals with cancer, and the roles of competing risks (first of all due to death cases) is presented.